Lab 2 : AssurExperts INC
¶

Travail elaboré par :

Mohamed Yassine Hakimi
Iheb Jridi
Baha Khayatti
Amal Chaari
Ameni Lefi

Compréhension métier

Les entreprises utilisent l’e-mailing comme outil de marketing direct et unidirectionnel pour informer sur leur actualité, leurs nouveautés,leurs services ou autres. Les e-mailings s’inscrivent ainsi dans une campagne marketing de l’entreprise qui vise à renforcer un message donné. En général, les destinataires des campagnes d’e-mailings sont les clients et ont donc de fortes probabilités d’être intéressés par les services et/ou produits. En revanche, un envoi abusif peut exemplifier un dérangement des clients, qui deviennent progressivement hostiles ou désintéressés à chaque réception.

Pour ce faire nous avons:

1- decrit les différents comportements des clients .

2- Mettre en place une modélisation qui servira à maximiser le nombre des clients réceptifs en ciblant au plus la moitié de la clientèle active.

3- ciblé la clientèle active et receptif aux offres de la compagnie.

Compréhension des données

import numpy as np
import pandas as pd
import plotly.plotly as py
import plotly.offline as pf
from plotly.offline import init_notebook_mode 
import plotly.graph_objs as go
import plotly.figure_factory as ff
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot

import plotly.figure_factory as F
import seaborn as sns
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import classification_report
from sklearn.feature_selection import RFE, f_regression
from sklearn.linear_model import (LinearRegression, Ridge, Lasso, RandomizedLasso)
import xgboost as xgb
from lightgbm import LGBMClassifier
from sklearn.svm import SVC
from sklearn.model_selection import cross_validate
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeClassifier
import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans

from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules

import plotly
plotly.tools.set_credentials_file(username='iheb.jridi', api_key='6MSvFnd4zja3zqQlzjqb')

init_notebook_mode()

Importation des données:¶

data = pd.read_table('C:/Users/AMAL/Desktop/1 SEM/ML/LAB 2/AssurancExpertsInc.txt')

data.head()

Ce dataset comporte 9822 individus. c'est un échantillon de données représentatif décrivant 9822 clients actifs chez AssurExperts Inc durant les vingt dernières années

data.describe()

data.dtypes.value_counts()

int64     85
object     2
dtype: int64

data.CLASS.value_counts()

No     9236
Yes     586
Name: CLASS, dtype: int64

sns.countplot(data['CLASS']);

yes = data[data['CLASS']=='Yes']
no = data[data['CLASS']=='No']

yes.SD1.replace((0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41), ("Nan","High Income, expensive child","Very Important Provincials","High status seniors","Affluent senior apartments","Mixed seniors","Career and childcare","Dinki's (double income no kids)","Middle class families","Modern, complete families","Stable family","Family starters","Affluent young families","Young all american family","Junior cosmopolitan","Senior cosmopolitans","Students in apartments","Fresh masters in the city","Single youth","Suburban youth","Etnically diverse","Young urban have-nots","Mixed apartment dwellers","Young and rising","Young, low educated","Young seniors in the city","Own home elderly","Seniors in apartments","Residential elderly","Porchless seniors: no front yard","Religious elderly singles","Low income catholics","Mixed seniors","Lower class large families","Large family, employed child","Village families","Couples with teens 'Married with children","Mixed small town dwellers","Traditional families","Large religous families","Large family farms","Mixed rurals"), inplace=True)
no.SD1.replace((0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41), ("Nan","High Income, expensive child","Very Important Provincials","High status seniors","Affluent senior apartments","Mixed seniors","Career and childcare","Dinki's (double income no kids)","Middle class families","Modern, complete families","Stable family","Family starters","Affluent young families","Young all american family","Junior cosmopolitan","Senior cosmopolitans","Students in apartments","Fresh masters in the city","Single youth","Suburban youth","Etnically diverse","Young urban have-nots","Mixed apartment dwellers","Young and rising","Young, low educated","Young seniors in the city","Own home elderly","Seniors in apartments","Residential elderly","Porchless seniors: no front yard","Religious elderly singles","Low income catholics","Mixed seniors","Lower class large families","Large family, employed child","Village families","Couples with teens 'Married with children","Mixed small town dwellers","Traditional families","Large religous families","Large family farms","Mixed rurals"), inplace=True)
yes.SD5.replace((0,1,2,3,4,5,6,7,8,9,10),("Nan","Successful hedonists","Driven Growers","Average Family","Career Loners","Living well","Cruising Seniors","Retired and Religeous","Family with grown ups","Conservative families","Farmers"), inplace=True)
no.SD5.replace((0,1,2,3,4,5,6,7,8,9,10),("Nan","Successful hedonists","Driven Growers","Average Family","Career Loners","Living well","Cruising Seniors","Retired and Religeous","Family with grown ups","Conservative families","Farmers"), inplace=True)
yes.SD4.replace((1,2,3,4,5,6),("1-20-30 years","2 30-40 years","3-40-50 years","4-50-60 years","5-60-70 years","6-70-80 years"), inplace=True)
no.SD4.replace((1,2,3,4,5,6),("1-20-30 years","2 30-40 years","3-40-50 years","4-50-60 years","5-60-70 years","6-70-80 years"), inplace=True)
no.PO44.replace((0,1,2,3,4,5,6,7,8,9),("f 0","f 1 – 49","f 50 – 99","f 100 – 199","f 200 – 499","f 500 – 999","f 1000 – 4999","f 5000 – 9999","f 10.000 - 19.999"," f 20.000 - ?"), inplace=True)
yes.PO44.replace((0,1,2,3,4,5,6,7,8,9),("f 0","f 1 – 49","f 50 – 99","f 100 – 199","f 200 – 499","f 500 – 999","f 1000 – 4999","f 5000 – 9999","f 10.000 - 19.999"," f 20.000 - ?"), inplace=True)

Exploration des données:¶

def yes_no(var):
    fig = {
      "data": [
        {
          "values": yes.loc[:,var].value_counts().values,
          "labels": yes.loc[:,var].value_counts().index,
          "domain": {"x": [0, .48]},
          "name": "Yes",
          "hoverinfo":"all",
          "hole": .4,
          "type": "pie"
        },
        {
          "values": no.loc[:,var].value_counts().values,
          "labels": no.loc[:,var].value_counts().index,
          "text":[var],
          "textposition":"inside",
          "domain": {"x": [.52, 1]},
          "name": "No",
          "hoverinfo":"all",
          "hole": .4,
          "type": "pie"
        }],
      "layout": {
            "title": var +" " +"Yes_No Comparaison",
            "annotations": [
                {
                    "font": {
                        "size": 20
                    },
                    "showarrow": True,
                    "text": "Yes",
                    "x": 0.20,
                    "y": 0.5
                },
                {
                    "font": {
                        "size": 20
                    },
                    "showarrow": True,
                    "text": "No",
                    "x": 0.8,
                    "y": 0.5
                }
            ]
        }
    }
    
    pf.iplot(fig, filename='donut')

SD1: Customer Subtype¶

yes_no('SD1')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux qui appartiennent à Lower class large families avec un pourcentage de 13.7% suivi par large family farms 12.3% pour les intéressés

SD4: MGEMLEEF Avg age¶

yes_no('SD4')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux qui appartiennent à la tranche d'age de 40-50 ans avec un pourcentage de 51.7% . Cette tranche d'age représente une majorité suivi par les 30-40 ans avec 26.6% ensuite les 50-60 ans avec un 17.9%.

SD5: Customer main type¶

yes_no('SD5')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux des family with grown ups avec 25%.

SD16: High level education¶

yes_no('SD16')

SD17: Medium level education¶

yes_no('SD17')

SD18: Lower level education¶

yes_no('SD18')

trace1 = go.Bar(
    x=data.CLASS,
    y=data.SD16,
    name='High level education'
)
trace2 = go.Bar(
    x=data.CLASS,
    y=data.SD17,
    name='medium level education'
)
trace3 = go.Bar(
    x=data.CLASS,
    y=data.SD18,
    name='Lower level education'
)

datag2 = [trace1, trace2,trace3]
layout = go.Layout(
    barmode='stack'
)

fig = go.Figure(data=datag2, layout=layout)
iplot(fig, filename='stacked-bar')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux qui ont un faible ou moyen niveau d'éducation

SD19,SD20,SD21,SD22,SD23 ET SD24¶

SD19: High status¶

yes_no('SD19')

SD20: Entrepreneur¶

yes_no('SD20')

SD1:Farmer¶

yes_no('SD21')

SD22: Middle management¶

yes_no('SD22')

SD23: skilled labourers¶

yes_no('SD23')

trace1 = go.Bar(
    x=data.CLASS,
    y=data.SD19,
    name='High status'
)
trace2 = go.Bar(
    x=data.CLASS,
    y=data.SD20,
    name='Entrepreneur'
)
trace3 = go.Bar(
    x=data.CLASS,
    y=data.SD21,
    name='Farmer'
)
trace4 = go.Bar(
    x=data.CLASS,
    y=data.SD22,
    name='Middle management'
)
trace5 = go.Bar(
    x=data.CLASS,
    y=data.SD23,
    name='skilled labourers'
)
trace6 = go.Bar(
    x=data.CLASS,
    y=data.SD24,
    name='unskilled labourers'
)
datag2 = [trace1, trace2,trace3,trace4, trace5,trace6]
layout = go.Layout(
    barmode='stack'
)

fig = go.Figure(data=datag2, layout=layout)
iplot(fig, filename='stacked-bar')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont des ouvriers non qualifiés,des ouvriers qualifiés,et les cadres intermédiaires.

SD25, SD26, SD27, SD28 et SD29¶

SD25: SOCIAL CLASS A¶

yes_no('SD25')

SD26: SOCIAL CLASS B1¶

yes_no('SD26')

SD27: SOCIAL CLASS B2¶

yes_no('SD27')

SD28: SOCIAL CLASS C¶

yes_no('SD28')

D29: SOCIAL CLASS D¶

yes_no('SD29')

trace1 = go.Bar(
    x=data.CLASS,
    y=data.SD25,
    name='SOCIAL CLASS A'
)
trace2 = go.Bar(
    x=data.CLASS,
    y=data.SD26,
    name='SOCIAL CLASS B1'
)
trace3 = go.Bar(
    x=data.CLASS,
    y=data.SD27,
    name='SOCIAL CLASS B2'
)
trace4 = go.Bar(
    x=data.CLASS,
    y=data.SD28,
    name='SOCIAL CLASS C'
)
trace5 = go.Bar(
    x=data.CLASS,
    y=data.SD29,
    name='SOCIAL CLASS D'
)
datag2 = [trace1, trace2, trace3, trace4, trace5]
layout = go.Layout(
    barmode='stack'
)

fig = go.Figure(data=datag2, layout=layout)
iplot(fig, filename='stacked-bar')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux qui appartiennent à une classe sociale C et ceux qui ont une maison et une voiture

SD30 et SD31¶

SD30:Rented house¶

yes_no('SD30')

SD31: Home Owners¶

yes_no("SD31")

trace1 = go.Bar(
    x=data.CLASS,
    y=data.SD30,
    name='Rented house'
)
trace2 = go.Bar(
    x=data.CLASS,
    y=data.SD31,
    name='Home Owners'
)

datag2 = [trace1, trace2]
layout = go.Layout(
    barmode='stack'
)

fig = go.Figure(data=datag2, layout=layout)
iplot(fig, filename='stacked-bar')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux sont propriétaires d'une maison .

SD35 et SD36¶

SD35: National Health Service¶

yes_no('SD35')

SD36: Private health insurance¶

yes_no('SD36')

trace1 = go.Bar(
    x=data.CLASS,
    y=data.SD35,
    name='National Health Service'
)
trace2 = go.Bar(
    x=data.CLASS,
    y=data.SD36,
    name='Private health insurance '
)

datag2 = [trace1, trace2]
layout = go.Layout(
    barmode='stack'
)

fig = go.Figure(data=datag2, layout=layout)
iplot(fig, filename='stacked-bar')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux qui ont un service de santé national.

SD42 et SD43¶

SD42:income¶

yes_no('SD42')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc,sont ceux qui ont un income plus élevé donc plus aisé financièrement.

SD43:Purchasing¶

yes_no('SD43')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux qui sont évidament plus aisés financièrement.

PO44:¶

yes_no('PO44')

On remarque que la contribution majoritaire chez les individus intéressés est de 50 - 99 et elle représente (55.1%). Alors que pour les individus non intéressés la contribution majoritaire est de 0 (61.2%) par la suite ces derniers sont plus assurés quant à la private third party.

PO47: Contribution car policies¶

yes_no('PO47')

On remarque que les clients intéressés ont des voitures beaucoup mieux assurés que les indiv non intéressés avec 71.7% , alors que 50.1% des clients non intéressées ont une contribution de 0.

PO59: Contribution fire policies¶

yes_no('PO59')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux qui ont contribué à une police d'incendie.

PO61: boat policies¶

yes_no('PO61')

On remarque que la modalité majoritaire est la contribution de 0, mais les clients intéressés montrent un pourentage de contribution plus important que celui des non intéressés.

PO65: private third party insurance¶

yes_no('PO65')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont plus porbables d'avoir 1 private third party insurance, alors que ceux qui ne le sont pas intéressés sont plus probable d'en avoir 0.

PO68: Number of car policies¶

yes_no('PO68')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont plus porbable d'avoir 1 car policy, alors que ceux qui ne sont pas intéressés sont plus probable d'en avoir 0.

PO82: Number of boat policies¶

yes_no('PO82')

On remarque, que les deux classes montrent une probablité forte d'avoir 0 boat policies, mais le plus grand nombre des clients actifs chez AssurExperts Inc, sont plus porbable d'avoir 1, boat policies, alors que ceux qui ne sont pas intéressés sont plus probable d'en avoir 0.

PO85: Number of social security insurance policies¶

yes_no('PO85')

On remarque, que les deux classes montrent une probablité forte d'avoir 0 social security insurance policies , mais le plus grand nombre des clients actifs chez AssurExperts Inc, sont plus porbable d'avoir 1,ecurity insurance policies, alors que ceux qui ne sont pas intéressés sont plus probable d'en avoir 0.

Conclusion: Profil des clients intéressés¶

D'après les conclusions ci-dessus, nous pouvons remarquer que le profil des clients intéressés de l'assurance caravane exige les criteres suivants:

Âge compris entre 30 et 60 ans
A une voiture
A une maison
Driven Growers, famille moyenne et famille avec adultes
Classe sociale A

Etude des corrélations¶

data.CLASS = pd.factorize(data.CLASS)[0]

correlations_class = data.corr(method="pearson").loc['CLASS'].sort_values()

correlations_class.tail(15)

SD25     0.064810
SD10     0.067168
SD32     0.070665
SD31     0.075283
PO61     0.075779
PO82     0.082763
SD16     0.084373
SD42     0.085122
PO65     0.091379
PO59     0.096709
PO44     0.098757
SD43     0.099018
PO68     0.126768
PO47     0.137053
CLASS    1.000000
Name: CLASS, dtype: float64

correlations_class.head(15)

SD18   -0.084835
SD37   -0.079035
SD30   -0.075743
SD34   -0.073395
SD1    -0.060742
SD35   -0.060607
SD5    -0.059306
SD12   -0.058787
SD21   -0.057811
SD13   -0.054349
SD24   -0.053767
SD29   -0.050652
SD28   -0.046842
PO54   -0.036581
PO75   -0.036398
Name: CLASS, dtype: float64

correlations = data.corr(method="pearson").abs()

uns = correlations.unstack()

sorted_correlations = uns.sort_values(kind="quicksort",ascending=False)
high_correlations = sorted_correlations[(0.8<sorted_correlations) & (sorted_correlations< 1.0)]

high_correlations = high_correlations.drop_duplicates()

high_correlations

SD30  SD31    0.999625
SD36  SD35    0.999381
SD1   SD5     0.992712
PO46  PO67    0.984484
PO44  PO65    0.981097
PO57  PO78    0.979788
PO75  PO54    0.967662
PO85  PO64    0.964774
PO51  PO72    0.962867
PO79  PO58    0.959882
PO81  PO60    0.942761
PO62  PO83    0.933068
PO49  PO70    0.923728
PO73  PO52    0.913831
PO74  PO53    0.908873
PO47  PO68    0.907506
PO66  PO45    0.906663
PO50  PO71    0.901122
PO56  PO77    0.898726
PO48  PO69    0.894309
PO61  PO82    0.894000
SD12  SD10    0.883251
PO59  PO80    0.869354
PO63  PO84    0.865845
PO76  PO55    0.852969
dtype: float64

Recursive Feature Elimination¶

Dans les cellules suivantes, nous allons utiliser RFE avec plusieurs modèles afain d'extraire l'importance de chaque feature.
Après, nous allons choisir les features avec une moyenne de ranking elevée.

Train = data.loc[data['STATUS'] == 'Learning']
Train = Train.drop(columns='STATUS', axis=1)
X_train = Train.drop(columns='CLASS', axis=1)
y_train = Train.CLASS

Test = data.loc[data['STATUS'] == 'Test']
Test = Test.drop(columns='STATUS', axis=1)
X_test = Test.drop(columns='CLASS', axis=1)
y_test = Test.CLASS

X=data.iloc[:,:-2]
Y = data.CLASS
colnames = X.columns

classifier = LinearRegression()
classifier.fit(X, Y)

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
         normalize=False)

from sklearn.preprocessing import MinMaxScaler
ranks = {}

def ranking(ranks, names, order=1):
    minmax = MinMaxScaler()
    ranks = minmax.fit_transform(order*np.array([ranks]).T).T[0]
    ranks = map(lambda x: round(x,2), ranks)
    return dict(zip(names, ranks))

rfe = RFE(classifier, n_features_to_select=1)

#Using Linear Regression
lr = LinearRegression(normalize=True)
lr.fit(X,Y)
ranks["LinReg"] = ranking(np.abs(lr.coef_), colnames)

# Using Ridge 
ridge = Ridge(alpha = 7)
ridge.fit(X,Y)
ranks['Ridge'] = ranking(np.abs(ridge.coef_), colnames)

# Using Lasso
lasso = Lasso(alpha=.05)
lasso.fit(X, Y)
ranks["Lasso"] = ranking(np.abs(lasso.coef_), colnames)

# Using Random Forest
rf = RandomForestRegressor(n_jobs=-1, n_estimators=50, verbose=3)
rf.fit(X,Y)
ranks["RF"] = ranking(rf.feature_importances_, colnames);

# Using XGBoost
xg = xgb.XGBClassifier(learning_rate=0.1, n_estimators=10, objective='binary:logistic')
xg.fit(X,Y)
ranks["XG"] = ranking(xg.feature_importances_, colnames);

# Using LightGBM
lgbm = LGBMClassifier(objective='binary',class_weight='balanced')
lgbm.fit(X, Y)
ranks["LGBM"] = ranking(lgbm.feature_importances_, colnames);

# Using SVM
svm = SVC(kernel='linear', probability=True,random_state = 5)
svm.fit(X=X, y=Y)
ranks["SVM"] = ranking(svm.coef_.ravel(), colnames);

[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.

building tree 1 of 50building tree 2 of 50building tree 3 of 50building tree 4 of 50building tree 5 of 50

building tree 6 of 50building tree 7 of 50

building tree 8 of 50


building tree 9 of 50
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building tree 12 of 50
building tree 13 of 50
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building tree 17 of 50
building tree 18 of 50
building tree 19 of 50
building tree 20 of 50
building tree 21 of 50
building tree 22 of 50
building tree 23 of 50
building tree 24 of 50
building tree 25 of 50
building tree 26 of 50
building tree 27 of 50
building tree 28 of 50
building tree 29 of 50
building tree 30 of 50
building tree 31 of 50
building tree 32 of 50
building tree 33 of 50building tree 34 of 50

[Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:    0.3s

building tree 35 of 50
building tree 36 of 50
building tree 37 of 50
building tree 38 of 50
building tree 39 of 50
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building tree 42 of 50
building tree 43 of 50
building tree 44 of 50
building tree 45 of 50
building tree 46 of 50
building tree 47 of 50
building tree 48 of 50
building tree 49 of 50
building tree 50 of 50

[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:    1.0s finished
C:\Users\yassi\Anaconda3\lib\site-packages\sklearn\utils\validation.py:590: DataConversionWarning:

Data with input dtype int32 was converted to float64 by MinMaxScaler.

s
r = {}
for name in colnames:
    r[name] = round(np.mean([ranks[method][name] 
                             for method in ranks.keys()]), 2)
 
methods = sorted(ranks.keys())
ranks["Mean"] = r
methods.append("Mean")
 
print("\t%s" % "\t".join(methods))
for name in colnames:
    print("%s\t%s" % (name, "\t".join(map(str, 
                         [ranks[method][name] for method in methods]))))

	LGBM	Lasso	LinReg	RF	Ridge	SVM	XG	Mean
SD1	1.0	0.15	0.0	0.56	0.01	0.85	0.06	0.38
SD2	0.11	0.0	0.01	0.08	0.02	0.86	0.0	0.15
SD3	0.17	0.0	0.0	0.13	0.01	0.85	0.0	0.17
SD4	0.14	0.0	0.02	0.15	0.06	0.85	0.0	0.17
SD5	0.01	0.0	0.01	0.23	0.04	0.86	0.0	0.16
SD6	0.24	0.0	0.0	0.14	0.01	0.85	0.0	0.18
SD7	0.47	0.0	0.0	0.27	0.01	0.86	0.0	0.23
SD8	0.28	0.0	0.01	0.19	0.02	0.85	0.0	0.19
SD9	0.35	0.0	0.0	0.24	0.0	0.86	0.0	0.21
SD10	0.35	0.0	0.02	0.19	0.05	0.85	0.0	0.21
SD11	0.3	0.0	0.0	0.12	0.01	0.85	0.0	0.18
SD12	0.28	0.0	0.01	0.14	0.04	0.85	0.0	0.19
SD13	0.27	0.0	0.02	0.18	0.05	0.85	0.0	0.2
SD14	0.33	0.0	0.02	0.28	0.05	0.85	0.0	0.22
SD15	0.37	0.0	0.01	0.24	0.04	0.85	0.0	0.22
SD16	0.26	0.0	0.01	0.26	0.04	0.86	0.0	0.2
SD17	0.27	0.0	0.0	0.27	0.01	0.86	0.0	0.2
SD18	0.31	0.0	0.01	0.25	0.04	0.85	0.31	0.25
SD19	0.31	0.0	0.0	0.21	0.01	0.86	0.0	0.2
SD20	0.16	0.0	0.01	0.17	0.02	0.86	0.0	0.17
SD21	0.32	0.0	0.03	0.09	0.08	0.85	0.0	0.2
SD22	0.49	0.0	0.0	0.29	0.01	0.86	0.44	0.3
SD23	0.46	0.0	0.0	0.24	0.0	0.86	0.0	0.22
SD24	0.43	0.0	0.0	0.25	0.0	0.86	0.0	0.22
SD25	0.19	0.0	0.01	0.23	0.01	0.86	0.0	0.19
SD26	0.4	0.0	0.01	0.23	0.03	0.86	0.0	0.22
SD27	0.37	0.0	0.01	0.2	0.02	0.86	0.0	0.21
SD28	0.46	0.0	0.0	0.24	0.01	0.86	0.0	0.22
SD29	0.32	0.0	0.0	0.16	0.0	0.86	0.0	0.19
SD30	0.39	0.01	0.06	0.15	0.18	0.84	0.0	0.23
SD31	0.13	0.0	0.06	0.15	0.16	0.84	0.0	0.19
SD32	0.5	0.0	0.03	0.2	0.08	0.85	0.0	0.24
SD33	0.25	0.0	0.02	0.19	0.06	0.85	0.0	0.2
SD34	0.27	0.0	0.02	0.16	0.05	0.85	0.0	0.19
SD35	0.34	0.0	0.16	0.15	0.34	0.73	0.0	0.25
SD36	0.04	0.0	0.16	0.13	0.34	0.74	0.0	0.2
SD37	0.6	0.0	0.01	0.2	0.04	0.85	0.5	0.31
SD38	0.39	0.0	0.01	0.25	0.03	0.86	0.0	0.22
SD39	0.32	0.0	0.01	0.23	0.02	0.86	0.0	0.21
SD40	0.23	0.0	0.0	0.18	0.01	0.86	0.0	0.18
SD41	0.16	0.0	0.03	0.08	0.09	0.85	0.0	0.17
SD42	0.31	0.0	0.02	0.15	0.05	0.85	0.0	0.2
SD43	0.55	0.0	0.01	0.45	0.03	0.85	0.31	0.31
PO44	0.41	0.0	0.09	0.54	0.24	0.85	0.0	0.3
PO45	0.14	0.0	0.03	0.04	0.07	0.85	0.0	0.16
PO46	0.12	0.0	0.04	0.01	0.12	0.88	0.0	0.17
PO47	0.71	1.0	0.02	0.55	0.07	0.86	0.62	0.55
PO48	0.04	0.0	0.0	0.02	0.0	1.0	0.0	0.15
PO49	0.19	0.0	0.02	0.09	0.05	0.85	0.0	0.17
PO50	0.0	0.0	0.05	0.0	0.09	0.84	0.0	0.14
PO51	0.03	0.0	0.09	0.04	0.16	0.86	0.0	0.17
PO52	0.14	0.0	0.02	0.04	0.04	0.85	0.0	0.16
PO53	0.0	0.0	0.04	0.0	0.09	0.84	0.0	0.14
PO54	0.27	0.0	0.01	0.16	0.03	0.86	0.0	0.19
PO55	0.22	0.0	0.03	0.23	0.08	0.85	0.0	0.2
PO56	0.07	0.0	0.04	0.01	0.09	0.78	0.0	0.14
PO57	0.07	0.0	0.49	0.09	0.5	0.91	0.0	0.29
PO58	0.05	0.0	0.08	0.03	0.11	0.85	0.12	0.18
PO59	0.91	0.0	0.04	1.0	0.11	0.85	1.0	0.56
PO60	0.0	0.0	0.13	0.01	0.45	0.8	0.06	0.21
PO61	0.17	0.0	0.0	0.11	0.18	0.84	0.75	0.29
PO62	0.06	0.0	0.05	0.08	0.06	0.86	0.0	0.16
PO63	0.03	0.0	0.1	0.04	0.22	0.84	0.0	0.18
PO64	0.1	0.0	0.09	0.11	0.19	0.85	0.0	0.19
PO65	0.0	0.0	0.13	0.37	0.33	0.86	0.0	0.24
PO66	0.01	0.0	0.05	0.03	0.11	0.87	0.0	0.15
PO67	0.01	0.0	0.02	0.01	0.04	0.74	0.0	0.12
PO68	0.12	0.0	0.0	0.39	0.0	0.85	0.0	0.19
PO69	0.0	0.0	0.08	0.01	0.21	0.0	0.0	0.04
PO70	0.0	0.0	0.04	0.09	0.11	0.86	0.0	0.16
PO71	0.0	0.0	0.12	0.0	0.16	0.85	0.0	0.16
PO72	0.0	0.0	0.09	0.03	0.11	0.82	0.0	0.15
PO73	0.0	0.0	0.06	0.02	0.15	0.85	0.0	0.15
PO74	0.0	0.0	0.06	0.0	0.09	0.85	0.0	0.14
PO75	0.04	0.0	0.03	0.11	0.06	0.85	0.0	0.16
PO76	0.08	0.0	0.09	0.28	0.24	0.85	0.12	0.24
PO77	0.0	0.0	0.05	0.01	0.07	0.95	0.0	0.15
PO78	0.0	0.0	1.0	0.04	0.69	0.71	0.0	0.35
PO79	0.0	0.0	0.31	0.01	0.3	0.87	0.0	0.21
PO80	0.08	0.0	0.07	0.29	0.18	0.86	0.0	0.21
PO81	0.0	0.0	0.84	0.01	0.46	0.97	0.0	0.33
PO82	0.0	0.0	0.59	0.1	1.0	0.89	0.0	0.37
PO83	0.03	0.0	0.09	0.12	0.19	0.86	0.0	0.18
PO84	0.0	0.0	0.17	0.02	0.35	0.88	0.0	0.2
PO85	0.0	0.0	0.17	0.06	0.24	0.85	0.0	0.19

meanplot = pd.DataFrame(list(r.items()), columns= ['Feature','Mean Ranking'])


meanplot = meanplot.sort_values('Mean Ranking', ascending=False)

sns.factorplot(x="Mean Ranking", y="Feature", data = meanplot, kind="bar", 
               size=14, aspect=1.9, palette='coolwarm')

C:\Users\yassi\Anaconda3\lib\site-packages\seaborn\categorical.py:3666: UserWarning:

The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.

C:\Users\yassi\Anaconda3\lib\site-packages\seaborn\categorical.py:3672: UserWarning:

The `size` paramter has been renamed to `height`; please update your code.

<seaborn.axisgrid.FacetGrid at 0x184a8eb2208>

Features Selected :¶

Le ranking graph ci-dessus nous permet de dégager les 31 premières features selon leurs moyennes de ranking.
On peut constater aussi que les features PO59, PO47, SD1 et PO82 ont une importance très elevée par rapport aux autres features.
Peut-on encore vérifier l'importance de ces features ?

Feature Selection Using Clustering¶

Nous allons utiliser K-means afin de diviser ceux qui ont répondu par "Yes" en deux clusters.
Cette technique va nous permettre de voir la répartition de ceux qui ont dit "Yes" sur deux clusters pour mieux comprendre laquelle entre eux est la plus décisive.

df_yes = data.loc[data.CLASS == 1]
df_yes = df_yes.drop('STATUS',1)

kmeans = KMeans(n_clusters=2).fit(df_yes)

df_yes['Cluster'] = kmeans.labels_

df_yes.head()

plt.scatter(df_yes.PO47, df_yes.Cluster)

<matplotlib.collections.PathCollection at 0x184aca63ba8>

plt.scatter(df_yes.PO59, df_yes.Cluster)

<matplotlib.collections.PathCollection at 0x184ac98e978>

plt.scatter(df_yes.PO82, df_yes.Cluster)

<matplotlib.collections.PathCollection at 0x184acad7588>

plt.scatter(df_yes.SD1, df_yes.Cluster)

<matplotlib.collections.PathCollection at 0x184acb34588>

Interprétation¶

La feature SD1 est l'une des variables les plus importantes dans le choix d'une assurance caravane. Elle peut même nous donner une idée sur les profils susceptibles à chosir ce type d'assurances.

selected_features = meanplot.head(31).Feature

df_reduced = data[selected_features]

df_reduced['CLASS'] = data.CLASS

df_reduced.to_csv('df_reduced.csv', sep='\t')

df_reduced.head()

Associativité:¶

ass_yes = pd.read_csv('df_reduced.csv', header=0, sep='\t')
ass_yes=ass_yes.loc[ass_yes['CLASS'] == 1]

ass_yes = ass_yes.drop(columns='CLASS', axis=1)

ass_yes = ass_yes.drop(columns='Unnamed: 0', axis=1)

ass_yes.head()

Dummy variable:¶

def process_var(var):
    global ass_yes
    # encoding into 3 categories:
    var_dummies = pd.get_dummies(ass_yes[var], prefix=var)
    
    # adding dummy variables
    ass_yes = pd.concat([ass_yes,var_dummies],axis=1)
    # removing "Pclass"
    ass_yes.drop(var,axis=1,inplace=True)

for v in ass_yes.columns:
    process_var(v)

ass_yes.head()

Apriori:¶

#Apriori
freq_items = apriori(ass_yes, min_support = 0.64, use_colnames=True)
rules = association_rules(freq_items, metric="confidence", min_threshold=0.6)
#sort the rules in non-decreasing lift value
rules = rules.sort_values(ascending=False, by="confidence")

rules

Classement des règles selon le niveau de confiance.¶

Les règles les plus précises sont: (PO61_0, PO60_0, PO57_0) ===> (PO78_0, PO81_0, PO82_0)

(PO61_0, PO47_6, PO81_0, PO57_0)===> (PO78_0, PO60_0, PO82_0)

(PO60_0, PO57_0, PO82_0)===>(PO78_0, PO61_0, PO81_0)

(PO78_0, PO60_0, PO82_0))===>(PO61_0, PO81_0, PO57_0)

(PO78_0, PO60_0, PO61_0)===>((PO81_0, PO82_0, PO57_0)

PO61_0 : les clients n'ayant pas une Contribution boat policies.

PO60_0: les clients ayant une Contribution surfboard policies égale à 0.

PO57_O: les clients ayant une Contribution family accidents insurance policies égale à 0.

PO78_0: les clients ayant Number of family accidents insurance policies égale à 0.

PO82_0 : les clients n'ayant pas de Contribution surfboard policies.

PO68_1 : les clients ayant un nombre de car policies égale à 1.

PO81_0 : les clients ayant un nombre of surfboard policies égale à 0.

PO61_0: les clients ayant une Contribution boat policies égale à 0.

PO47_6 : les clients ayant un PPERSAUT Contribution car policies égale à 6.

Cet output nous a permis de faire identifier les critères des clients qu’on doit les envoyer des mails .En effet, les clients n’ayant ni de Contribution surfboard policies, ni de Contribution Contribution boat policies , ni de un nombre of surfboard policies, un nombre de car policies qui est égale à 1 et un PPERSAUT Contribution car policies égale à 6 sont les plus intéressés d’avoir une assurance sur caravane.

Les 10 règles les plus « pertinentes »:¶

import mlxtend.plotting as mlplot

#draw a graph of associations
def draw_graph(rules, rules_to_show):
    import networkx as nx  
    G1 = nx.DiGraph()
   
    color_map=[]
    N = 50
    colors = np.random.rand(N)    
    strs=['R0', 'R1', 'R2', 'R3', 'R4', 'R5', 'R6', 'R7', 'R8','R9']   


    for i in range (rules_to_show):      
        G1.add_nodes_from(["R"+str(i)])
    
     
        for a in rules.iloc[i]["antecedents"]:

            G1.add_nodes_from([a])

            G1.add_edge(a, "R"+str(i), color=colors[i] , weight = 2)

        for c in rules.iloc[i]['consequents']:

            G1.add_nodes_from([c])

            G1.add_edge("R"+str(i), c, color=colors[i],  weight=2)

    for node in G1:
        found_a_string = False
        for item in strs: 
            if node==item:
                found_a_string = True
        if found_a_string:
            color_map.append('blue')
        else:
            color_map.append('red')       
 
 
   
    edges = G1.edges()
    colors = [G1[u][v]['color'] for u,v in edges]
    weights = [G1[u][v]['weight'] for u,v in edges]

    pos = nx.spring_layout(G1, k=16, scale=1)
    nx.draw(G1, pos, edges=edges, node_color = color_map, edge_color=colors, width=weights, font_size=16, with_labels=False)            

    for p in pos: 
        pos[p][1] += 0.07
    nx.draw_networkx_labels(G1, pos)
    plt.show()

draw_graph(rules, 10)

Scoring¶

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

data_scoring = pd.read_csv('df_reduced.csv', sep='\t')

data_scoring = data_scoring.drop(columns='Unnamed: 0', axis=1)
X = data_scoring.drop(['CLASS'],axis=1)
y = pd.DataFrame(data_scoring.iloc[:,-1])

X.head()

LightGBM¶

from lightgbm import LGBMClassifier
import xgboost as xgb
from sklearn.ensemble import RandomForestRegressor
lgbm = LGBMClassifier(objective='binary',class_weight='balanced')
lgbm.fit(X, y)

C:\Users\yassi\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py:219: DataConversionWarning:

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

C:\Users\yassi\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py:252: DataConversionWarning:

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

LGBMClassifier(boosting_type='gbdt', class_weight='balanced',
        colsample_bytree=1.0, importance_type='split', learning_rate=0.1,
        max_depth=-1, min_child_samples=20, min_child_weight=0.001,
        min_split_gain=0.0, n_estimators=100, n_jobs=-1, num_leaves=31,
        objective='binary', random_state=None, reg_alpha=0.0,
        reg_lambda=0.0, silent=True, subsample=1.0,
        subsample_for_bin=200000, subsample_freq=0)

y_pred=lgbm.predict(X)

proba=lgbm.predict_proba(X)

proba

array([[0.52481952, 0.47518048],
       [0.968437  , 0.031563  ],
       [0.90653892, 0.09346108],
       ...,
       [0.55206356, 0.44793644],
       [0.42561745, 0.57438255],
       [0.86828346, 0.13171654]])

pd.DataFrame(proba)

from sklearn.metrics import confusion_matrix,accuracy_score,precision_score,cohen_kappa_score,recall_score,auc
cm = confusion_matrix(y_pred,y)
print(cm)
print('accuracy_score :',accuracy_score(y,y_pred))
print('auc :',auc(y,y_pred,reorder=True))
print('precision_score : ',precision_score(y,y_pred))
print('recall_score :', recall_score(y,y_pred))
print('kappa_score :',cohen_kappa_score(y,y_pred))

[[7874    8]
 [1362  578]]
accuracy_score : 0.8605172062716351
auc : 0.5
precision_score :  0.2979381443298969
recall_score : 0.9863481228668942
kappa_score : 0.4029230199356675

C:\Users\yassi\Anaconda3\lib\site-packages\sklearn\metrics\ranking.py:104: DeprecationWarning:

The 'reorder' parameter has been deprecated in version 0.20 and will be removed in 0.22. It is recommended not to set 'reorder' and ensure that x is monotonic increasing or monotonic decreasing.

data_scored = proba[:,1]

data_scoring["Score"]=data_scored

dataa=data_scoring

data_sorted = dataa.sort_values(by='Score', ascending = False)
data_sorted.head()

vised_clients = data_sorted.iloc[0:dataa.shape[0]//2, :]
vised_clients

Pour maximiser le nombre des clients réceptifs AssurExperts Inc doit cibler les individus qui ont un score supérieur a 0.139975

FALSE_NEGATIVE=vised_clients.loc[(vised_clients['CLASS']==0)&(vised_clients['Score']>0.5)]
FALSE_NEGATIVE

FALSE_NEGATIVE.shape[0]/dataa.shape[0]*100

13.866829566279781

Le pourcentage des clients qui sont désintéressé par La police d’assurance sur caravane,mais qui sont suceptibles d'etre pour est de 13.86% parmi les clients active

Evaluation

Lorsque nous sommes arrivés au but, nous sommes passés à la révision comme étant une étape primordiale de l'évaluation alors on a remarqué qu'il y a des clients qui ont répondu par "NON" et il s'est avéré qu’ils peuvent être intéressés. De même, il y a ceux qui ont indiqué qu’ils ont un intérêt alors qu'ils ne sont pas forcément intéressés.

faut-il donc envoyer des mails uniquement aux clients qui ont répondu par 'OUI' au sondage?

Deploiement

L’une des questions à envisager est celle de cibler les clients qui ont répondu par 'NON', mais qui peuvent être intéresses d'après notre modèle.

Dans ce cas, on peut trouver une solution pour que la compagnie d'assurance ne gaspillera plus d'argent en limitant le nombre de mails envoyés.

	SD1	SD2	SD3	SD4	SD5	SD6	SD7	SD8	SD9	SD10	...	PO76	PO77	PO78	PO79	PO80	PO81	PO82	PO83	PO84	PO85
count	9822.000000	9822.000000	9822.000000	9822.000000	9822.000000	9822.000000	9822.000000	9822.000000	9822.000000	9822.000000	...	9822.000000	9822.000000	9822.000000	9822.000000	9822.000000	9822.000000	9822.000000	9822.00000	9822.000000	9822.000000
mean	24.253207	1.108735	2.677561	2.996437	5.779067	0.700672	4.637650	1.050092	3.262981	6.188964	...	0.079821	0.004582	0.007941	0.004276	0.574018	0.000916	0.005091	0.03146	0.008450	0.013846
std	12.918058	0.412101	0.780701	0.804660	2.874148	1.015107	1.721212	1.011156	1.606287	1.896070	...	0.384431	0.067535	0.088764	0.071224	0.561255	0.030258	0.077996	0.20907	0.092647	0.117728
min	1.000000	1.000000	1.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000
25%	10.000000	1.000000	2.000000	2.000000	3.000000	0.000000	4.000000	0.000000	2.000000	5.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000
50%	30.000000	1.000000	3.000000	3.000000	7.000000	0.000000	5.000000	1.000000	3.000000	6.000000	...	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.00000	0.000000	0.000000
75%	35.000000	1.000000	3.000000	3.000000	8.000000	1.000000	6.000000	2.000000	4.000000	7.000000	...	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.00000	0.000000	0.000000
max	41.000000	10.000000	6.000000	6.000000	10.000000	9.000000	9.000000	5.000000	9.000000	9.000000	...	8.000000	1.000000	1.000000	2.000000	7.000000	1.000000	2.000000	4.00000	2.000000	2.000000

	PO59_0	PO59_3	PO59_4	PO47_0	PO47_6	...	SD10_7	SD10_9	PO60_0
41	0	0	1	0	1	...	0	1	1
45	0	1	0	0	1	...	1	0	1
57	0	0	1	1	0	...	1	0	1
97	1	0	0	1	0	...	1	0	1
98	1	0	0	1	0	...	1	0	1

	SD1	SD2	SD3	SD4	SD5	SD6	SD7	SD8	SD9	SD10	...	PO80	CLASS	STATUS
0	33	1	3	2	8	0	5	1	3	7	...	1	No	Learning
1	37	1	2	2	8	1	4	1	4	6	...	1	No	Learning
2	37	1	2	2	8	0	4	2	4	3	...	1	No	Learning
3	9	1	3	3	3	2	3	2	4	5	...	1	No	Learning
4	40	1	4	2	10	1	4	1	4	7	...	1	No	Learning

	SD1	SD2	SD3	SD4	SD5	SD6	SD7	SD8	SD9	SD10	...	PO79	PO80	PO83	CLASS	Cluster
41	11	1	3	3	3	2	7	0	0	9	...	0	2	1	1	1
45	38	1	3	3	9	0	5	1	3	7	...	0	1	0	1	0
57	12	1	3	2	3	0	6	0	3	7	...	1	1	0	1	1
97	9	1	2	3	3	0	6	1	2	7	...	0	0	0	1	1
98	36	1	2	4	8	2	4	2	2	7	...	0	0	0	1	0

	PO59	PO47	SD1	SD37	SD43	PO44	SD22	...	SD26	SD15	SD14	SD23	PO80	SD39	SD10
0	5	6	33	0	3	0	2	...	1	6	2	5	1	5	7
1	2	0	37	2	4	2	5	...	2	5	4	0	1	5	6
2	2	6	37	4	4	2	7	...	5	2	4	0	1	0	3
3	2	6	9	1	4	0	3	...	2	4	3	1	1	3	5
4	6	0	40	0	3	0	0	...	0	4	4	0	1	9	7

	PO59	PO47	SD1	SD37	SD43	PO44	SD22	...	SD28	SD26	SD15	SD14	SD23	PO80	PO79	SD39	SD10
41	4	6	11	2	6	2	4	...	2	2	4	3	0	2	0	3	9
45	3	6	38	4	4	2	2	...	5	1	5	2	3	1	0	1	7
57	4	0	12	2	7	2	5	...	4	3	6	2	2	1	1	4	7
97	0	0	9	0	4	0	5	...	0	8	4	1	1	0	0	4	7
98	0	0	36	2	3	0	3	...	4	1	3	4	1	0	0	3	7

	PO59_0	PO59_3	PO59_4	PO47_0	PO47_6	...	SD10_7	SD10_9	PO60_0
41	0	0	1	0	1	...	0	1	1
45	0	1	0	0	1	...	1	0	1
57	0	0	1	1	0	...	1	0	1
97	1	0	0	1	0	...	1	0	1
98	1	0	0	1	0	...	1	0	1

	PO59	PO47	SD1	SD37	SD43	PO44	SD22	SD18	SD35	SD32	PO65	SD7	SD30	SD24	SD38	SD28	SD26	SD15	SD14	SD23	PO80	SD39	SD10
0	5	6	33	0	3	0	2	7	8	8	0	5	1	2	4	6	1	6	2	5	1	5	7
1	2	0	37	2	4	2	5	4	6	7	2	4	2	4	0	5	2	5	4	0	1	5	6
2	2	6	37	4	4	2	7	4	9	7	1	4	7	2	5	4	5	2	4	0	1	0	3
3	2	6	9	1	4	0	3	2	7	9	0	3	5	2	5	4	2	4	3	1	1	3	5
4	6	0	40	0	3	0	0	0	5	6	0	4	4	0	0	0	0	4	4	0	1	9	7

	0	1
0	0.524820	0.475180
1	0.968437	0.031563
2	0.906539	0.093461
3	0.780641	0.219359
4	0.997308	0.002692
5	0.912865	0.087135
6	0.716450	0.283550
7	0.351331	0.648669
8	0.272607	0.727393
9	0.955121	0.044879
10	0.849291	0.150709
11	0.593525	0.406475
12	0.302819	0.697181
13	0.963665	0.036335
14	0.989964	0.010036
15	0.774084	0.225916
16	0.904463	0.095537
17	0.172269	0.827731
18	0.720106	0.279894
19	0.987891	0.012109
20	0.327620	0.672380
21	0.537565	0.462435
22	0.553712	0.446288
23	0.959719	0.040281
24	0.449236	0.550764
25	0.529375	0.470625
26	0.186065	0.813935
27	0.987739	0.012261
28	0.998768	0.001232
29	0.980570	0.019430
30	0.766218	0.233782
31	0.962734	0.037266
32	0.560563	0.439437
33	0.815606	0.184394
34	0.455897	0.544103
35	0.698094	0.301906
36	0.157021	0.842979
37	0.993736	0.006264
38	0.980451	0.019549
39	0.888168	0.111832
40	0.993528	0.006472
41	0.178272	0.821728
42	0.980151	0.019849
43	0.529438	0.470562
44	0.555774	0.444226
45	0.235698	0.764302
46	0.934649	0.065351
47	0.878607	0.121393
48	0.395747	0.604253
49	0.955302	0.044698
50	0.554312	0.445688
51	0.941529	0.058471
52	0.928429	0.071571
53	0.980574	0.019426
54	0.794889	0.205111
55	0.347125	0.652875
56	0.983532	0.016468
57	0.109923	0.890077
58	0.933581	0.066419
59	0.981847	0.018153
60	0.847474	0.152526
61	0.691495	0.308505
62	0.841520	0.158480
63	0.995036	0.004964
64	0.994965	0.005035
65	0.185419	0.814581
66	0.574863	0.425137
67	0.310302	0.689698
68	0.845276	0.154724
69	0.952321	0.047679
70	0.974687	0.025313
71	0.972633	0.027367
72	0.885097	0.114903
73	0.441281	0.558719
74	0.471927	0.528073
75	0.578851	0.421149
76	0.914145	0.085855
77	0.862178	0.137822
78	0.872637	0.127363
79	0.464694	0.535306
80	0.831685	0.168315
81	0.993681	0.006319
82	0.854885	0.145115
83	0.948049	0.051951
84	0.972496	0.027504
85	0.288432	0.711568
86	0.833930	0.166070
87	0.631249	0.368751
88	0.847604	0.152396
89	0.715554	0.284446
90	0.886166	0.113834
91	0.618845	0.381155
92	0.536389	0.463611
93	0.984655	0.015345
94	0.611839	0.388161
95	0.973654	0.026346
96	0.888421	0.111579
97	0.323125	0.676875
98	0.261999	0.738001
99	0.992366	0.007634
100	0.916942	0.083058
101	0.867468	0.132532
102	0.762204	0.237796
103	0.828980	0.171020
104	0.336029	0.663971
105	0.984916	0.015084
106	0.904212	0.095788
107	0.868891	0.131109
108	0.747290	0.252710
109	0.452716	0.547284
110	0.869672	0.130328
111	0.984858	0.015142
112	0.715908	0.284092
113	0.690431	0.309569
114	0.377281	0.622719
115	0.974918	0.025082
116	0.388048	0.611952
117	0.919603	0.080397
118	0.308976	0.691024
119	0.991703	0.008297
120	0.935598	0.064402
121	0.842233	0.157767
122	0.928689	0.071311
123	0.857326	0.142674
124	0.519935	0.480065
125	0.801069	0.198931
126	0.304276	0.695724
127	0.130846	0.869154
128	0.885392	0.114608
129	0.923947	0.076053
130	0.531405	0.468595
131	0.532100	0.467900
132	0.982397	0.017603
133	0.951212	0.048788
134	0.954475	0.045525
135	0.905708	0.094292
136	0.617336	0.382664
137	0.949494	0.050506
138	0.843637	0.156363
139	0.960863	0.039137
140	0.978498	0.021502
141	0.431868	0.568132
142	0.782674	0.217326
143	0.935235	0.064765
144	0.973969	0.026031
145	0.892143	0.107857
146	0.801631	0.198369
147	0.708866	0.291134
148	0.983047	0.016953
149	0.327874	0.672126
150	0.402199	0.597801
151	0.657754	0.342246
152	0.933008	0.066992
153	0.831940	0.168060
154	0.638248	0.361752
155	0.826001	0.173999
156	0.121798	0.878202
157	0.806011	0.193989
158	0.846003	0.153997
159	0.636357	0.363643
160	0.974345	0.025655
161	0.949993	0.050007
162	0.527000	0.473000
163	0.981943	0.018057
164	0.907166	0.092834
165	0.332739	0.667261
166	0.873143	0.126857
167	0.993577	0.006423
168	0.857338	0.142662
169	0.886510	0.113490
170	0.941203	0.058797
171	0.279613	0.720387
172	0.816551	0.183449
173	0.800431	0.199569
174	0.355208	0.644792
175	0.415196	0.584804
176	0.837839	0.162161
177	0.865732	0.134268
178	0.677383	0.322617
179	0.101583	0.898417
180	0.854708	0.145292
181	0.848495	0.151505
182	0.129537	0.870463
183	0.397224	0.602776
184	0.632632	0.367368
185	0.999034	0.000966
186	0.708610	0.291390
187	0.220716	0.779284
188	0.984033	0.015967
189	0.796576	0.203424
190	0.837388	0.162612
191	0.526876	0.473124
192	0.896100	0.103900
193	0.928168	0.071832
194	0.148528	0.851472
195	0.746351	0.253649
196	0.973751	0.026249
197	0.644963	0.355037
198	0.839148	0.160852
199	0.552020	0.447980
200	0.936213	0.063787
201	0.978608	0.021392
202	0.689543	0.310457
203	0.144150	0.855850
204	0.660029	0.339971
205	0.925488	0.074512
206	0.551938	0.448062
207	0.966545	0.033455
208	0.760420	0.239580
209	0.769193	0.230807
210	0.983808	0.016192
211	0.871573	0.128427
212	0.629875	0.370125
213	0.437553	0.562447
214	0.778940	0.221060
215	0.993330	0.006670
216	0.976674	0.023326
217	0.391486	0.608514
218	0.675494	0.324506
219	0.804537	0.195463
220	0.205260	0.794740
221	0.990415	0.009585
222	0.700868	0.299132
223	0.952711	0.047289
224	0.966505	0.033495
225	0.869488	0.130512
226	0.097047	0.902953
227	0.575272	0.424728
228	0.857459	0.142541
229	0.775172	0.224828
230	0.971377	0.028623
231	0.864614	0.135386
232	0.392450	0.607550
233	0.998814	0.001186
234	0.122350	0.877650
235	0.271423	0.728577
236	0.993582	0.006418
237	0.898103	0.101897
238	0.972941	0.027059
239	0.312945	0.687055
240	0.216524	0.783476
241	0.775423	0.224577
242	0.996243	0.003757
243	0.858076	0.141924
244	0.738629	0.261371
245	0.502583	0.497417
246	0.130420	0.869580
247	0.437674	0.562326
248	0.351331	0.648669
249	0.694090	0.305910
...	...	...
9572	0.898685	0.101315
9573	0.981744	0.018256
9574	0.848588	0.151412
9575	0.936389	0.063611
9576	0.871209	0.128791
9577	0.981417	0.018583
9578	0.407309	0.592691
9579	0.974211	0.025789
9580	0.964445	0.035555
9581	0.208989	0.791011
9582	0.936847	0.063153
9583	0.933836	0.066164
9584	0.975982	0.024018
9585	0.738007	0.261993
9586	0.380791	0.619209
9587	0.992399	0.007601
9588	0.977986	0.022014
9589	0.945559	0.054441
9590	0.967035	0.032965
9591	0.938836	0.061164
9592	0.864157	0.135843
9593	0.889933	0.110067
9594	0.225547	0.774453
9595	0.938791	0.061209
9596	0.768169	0.231831
9597	0.314105	0.685895
9598	0.869171	0.130829
9599	0.211877	0.788123
9600	0.890059	0.109941
9601	0.995958	0.004042
9602	0.305899	0.694101
9603	0.315366	0.684634
9604	0.950677	0.049323
9605	0.350822	0.649178
9606	0.604067	0.395933
9607	0.911368	0.088632
9608	0.984028	0.015972
9609	0.878751	0.121249
9610	0.498166	0.501834
9611	0.296840	0.703160
9612	0.983777	0.016223
9613	0.812859	0.187141
9614	0.933768	0.066232
9615	0.470796	0.529204
9616	0.686110	0.313890
9617	0.956559	0.043441
9618	0.924393	0.075607
9619	0.878961	0.121039
9620	0.962052	0.037948
9621	0.945910	0.054090
9622	0.159395	0.840605
9623	0.344133	0.655867
9624	0.486350	0.513650
9625	0.997705	0.002295
9626	0.982518	0.017482
9627	0.673974	0.326026
9628	0.935807	0.064193
9629	0.500230	0.499770
9630	0.979897	0.020103
9631	0.419708	0.580292
9632	0.745086	0.254914
9633	0.945247	0.054753
9634	0.932397	0.067603
9635	0.690919	0.309081
9636	0.913596	0.086404
9637	0.902024	0.097976
9638	0.482544	0.517456
9639	0.965405	0.034595
9640	0.908222	0.091778
9641	0.956662	0.043338
9642	0.881299	0.118701
9643	0.928433	0.071567
9644	0.870824	0.129176
9645	0.947655	0.052345
9646	0.964830	0.035170
9647	0.850362	0.149638
9648	0.793045	0.206955
9649	0.892005	0.107995
9650	0.894000	0.106000
9651	0.970192	0.029808
9652	0.987206	0.012794
9653	0.482597	0.517403
9654	0.791370	0.208630
9655	0.924987	0.075013
9656	0.966055	0.033945
9657	0.961256	0.038744
9658	0.132759	0.867241
9659	0.972459	0.027541
9660	0.243985	0.756015
9661	0.607839	0.392161
9662	0.468542	0.531458
9663	0.906206	0.093794
9664	0.267279	0.732721
9665	0.959288	0.040712
9666	0.800669	0.199331
9667	0.826001	0.173999
9668	0.993473	0.006527
9669	0.958621	0.041379
9670	0.918291	0.081709
9671	0.962988	0.037012
9672	0.856371	0.143629
9673	0.541991	0.458009
9674	0.736327	0.263673
9675	0.808247	0.191753
9676	0.782590	0.217410
9677	0.918244	0.081756
9678	0.515799	0.484201
9679	0.977779	0.022221
9680	0.271639	0.728361
9681	0.495266	0.504734
9682	0.994087	0.005913
9683	0.963526	0.036474
9684	0.293502	0.706498
9685	0.941531	0.058469
9686	0.907191	0.092809
9687	0.305212	0.694788
9688	0.965958	0.034042
9689	0.802338	0.197662
9690	0.975352	0.024648
9691	0.946205	0.053795
9692	0.950088	0.049912
9693	0.409938	0.590062
9694	0.893199	0.106801
9695	0.817968	0.182032
9696	0.931399	0.068601
9697	0.951459	0.048541
9698	0.963227	0.036773
9699	0.971025	0.028975
9700	0.946841	0.053159
9701	0.969224	0.030776
9702	0.952764	0.047236
9703	0.292238	0.707762
9704	0.382435	0.617565
9705	0.948900	0.051100
9706	0.446818	0.553182
9707	0.899856	0.100144
9708	0.997535	0.002465
9709	0.951750	0.048250
9710	0.678431	0.321569
9711	0.279613	0.720387
9712	0.647002	0.352998
9713	0.168354	0.831646
9714	0.725912	0.274088
9715	0.983912	0.016088
9716	0.286962	0.713038
9717	0.989202	0.010798
9718	0.116937	0.883063
9719	0.970505	0.029495
9720	0.993630	0.006370
9721	0.883151	0.116849
9722	0.864664	0.135336
9723	0.828012	0.171988
9724	0.481226	0.518774
9725	0.864664	0.135336
9726	0.616071	0.383929
9727	0.966023	0.033977
9728	0.388806	0.611194
9729	0.828328	0.171672
9730	0.749044	0.250956
9731	0.824922	0.175078
9732	0.260939	0.739061
9733	0.349579	0.650421
9734	0.349201	0.650799
9735	0.663648	0.336352
9736	0.578461	0.421539
9737	0.673328	0.326672
9738	0.921738	0.078262
9739	0.917782	0.082218
9740	0.431982	0.568018
9741	0.373327	0.626673
9742	0.903023	0.096977
9743	0.406572	0.593428
9744	0.954647	0.045353
9745	0.890054	0.109946
9746	0.978382	0.021618
9747	0.909636	0.090364
9748	0.407742	0.592258
9749	0.956784	0.043216
9750	0.994443	0.005557
9751	0.870050	0.129950
9752	0.908213	0.091787
9753	0.967342	0.032658
9754	0.843423	0.156577
9755	0.827880	0.172120
9756	0.355309	0.644691
9757	0.952400	0.047600
9758	0.764952	0.235048
9759	0.889933	0.110067
9760	0.969329	0.030671
9761	0.977932	0.022068
9762	0.700128	0.299872
9763	0.796486	0.203514
9764	0.462361	0.537639
9765	0.570077	0.429923
9766	0.989350	0.010650
9767	0.743354	0.256646
9768	0.909040	0.090960
9769	0.799849	0.200151
9770	0.482544	0.517456
9771	0.615482	0.384518
9772	0.882604	0.117396
9773	0.969319	0.030681
9774	0.977639	0.022361
9775	0.263425	0.736575
9776	0.765002	0.234998
9777	0.921206	0.078794
9778	0.686110	0.313890
9779	0.987069	0.012931
9780	0.787080	0.212920
9781	0.867536	0.132464
9782	0.202257	0.797743
9783	0.726960	0.273040
9784	0.956990	0.043010
9785	0.989315	0.010685
9786	0.792017	0.207983
9787	0.577036	0.422964
9788	0.991468	0.008532
9789	0.508586	0.491414
9790	0.990333	0.009667
9791	0.987572	0.012428
9792	0.924534	0.075466
9793	0.810748	0.189252
9794	0.987235	0.012765
9795	0.721281	0.278719
9796	0.347125	0.652875
9797	0.808393	0.191607
9798	0.975281	0.024719
9799	0.995265	0.004735
9800	0.691166	0.308834
9801	0.887641	0.112359
9802	0.546994	0.453006
9803	0.962583	0.037417
9804	0.826878	0.173122
9805	0.304228	0.695772
9806	0.368704	0.631296
9807	0.876524	0.123476
9808	0.441159	0.558841
9809	0.904553	0.095447
9810	0.229635	0.770365
9811	0.504587	0.495413
9812	0.974508	0.025492
9813	0.923758	0.076242
9814	0.887444	0.112556
9815	0.658216	0.341784
9816	0.784786	0.215214
9817	0.898712	0.101288
9818	0.173304	0.826696
9819	0.552064	0.447936
9820	0.425617	0.574383
9821	0.868283	0.131717

	PO59	PO47	SD1	PO82	PO78	SD37	SD43	PO44	SD22	PO57	PO61	SD18	SD35	SD32	PO65	SD7	SD30	SD24	SD38	SD28	SD26	SD15	SD14	SD23	PO80	SD39	SD10	CLASS	Score
8443	4	6	12	1	0	0	7	2	7	0	3	1	7	7	1	5	0	2	5	1	3	5	2	1	1	4	5	1	0.972952
6397	5	6	8	2	0	0	7	0	0	0	4	2	2	3	0	2	0	0	2	0	0	4	4	0	1	5	7	1	0.968750
7320	3	6	1	0	1	0	8	2	2	3	0	0	0	5	1	7	0	1	2	0	2	5	4	0	1	6	7	1	0.962462
2188	4	6	7	1	0	0	6	2	7	0	4	0	7	7	1	7	0	0	6	0	3	3	6	0	1	3	7	0	0.959862
1595	3	6	38	1	0	2	4	0	3	0	4	4	9	7	0	5	7	2	2	5	2	4	3	3	1	5	4	0	0.958136

	antecedents	consequents	antecedent support	consequent support	support	confidence	lift	leverage	conviction
6689	(PO79_0, PO61_0, PO47_6, PO60_0, PO57_0)	(PO78_0)	0.674061	0.977816	0.674061	1.000000	1.022688	0.014954	inf
782	(PO82_0, PO47_6, PO60_0)	(PO81_0)	0.694539	0.994881	0.694539	1.000000	1.005146	0.003556	inf
9816	(PO78_0, PO79_0, PO60_0, PO81_0, PO82_0)	(PO61_0, PO57_0)	0.940273	0.950512	0.940273	1.000000	1.052065	0.046532	inf
9817	(PO78_0, PO79_0, PO60_0, PO57_0, PO82_0)	(PO61_0, PO81_0)	0.940273	0.969283	0.940273	1.000000	1.031690	0.028882	inf
9818	(PO78_0, PO79_0, PO81_0, PO57_0, PO82_0)	(PO61_0, PO60_0)	0.940273	0.969283	0.940273	1.000000	1.031690	0.028882	inf
3076	(PO78_0, PO79_0, PO81_0, PO47_6)	(PO57_0)	0.694539	0.977816	0.694539	1.000000	1.022688	0.015408	inf
7995	(PO61_0, PO79_0, PO81_0, PO76_0)	(PO82_0, PO60_0)	0.877133	0.969283	0.877133	1.000000	1.031690	0.026943	inf
783	(PO81_0, PO47_6, PO82_0)	(PO60_0)	0.694539	0.994881	0.694539	1.000000	1.005146	0.003556	inf
7993	(PO61_0, PO79_0, PO76_0, PO60_0)	(PO81_0, PO82_0)	0.877133	0.969283	0.877133	1.000000	1.031690	0.026943	inf
1732	(PO61_0, PO79_0, PO60_0)	(PO82_0)	0.962457	0.972696	0.962457	1.000000	1.028070	0.026279	inf
7990	(PO79_0, PO60_0, PO81_0, PO76_0, PO82_0)	(PO61_0)	0.877133	0.972696	0.877133	1.000000	1.028070	0.023949	inf
7989	(PO79_0, PO61_0, PO81_0, PO76_0, PO82_0)	(PO60_0)	0.877133	0.994881	0.877133	1.000000	1.005146	0.004490	inf
9824	(PO79_0, PO61_0, PO60_0, PO81_0, PO57_0)	(PO78_0, PO82_0)	0.940273	0.950512	0.940273	1.000000	1.052065	0.046532	inf
7988	(PO79_0, PO61_0, PO60_0, PO76_0, PO82_0)	(PO81_0)	0.877133	0.994881	0.877133	1.000000	1.005146	0.004490	inf
9826	(PO79_0, PO61_0, PO60_0, PO57_0, PO82_0)	(PO78_0, PO81_0)	0.940273	0.972696	0.940273	1.000000	1.028070	0.025673	inf
7986	(PO79_0, PO61_0, PO60_0, PO81_0, PO76_0)	(PO82_0)	0.877133	0.972696	0.877133	1.000000	1.028070	0.023949	inf
1735	(PO79_0, PO82_0, PO60_0)	(PO61_0)	0.962457	0.972696	0.962457	1.000000	1.028070	0.026279	inf
9813	(PO78_0, PO79_0, PO61_0, PO81_0, PO82_0)	(PO57_0, PO60_0)	0.940273	0.972696	0.940273	1.000000	1.028070	0.025673	inf
1816	(PO79_0, PO78_0, PO81_0)	(PO57_0)	0.965870	0.977816	0.965870	1.000000	1.022688	0.021427	inf
8000	(PO79_0, PO82_0, PO76_0, PO60_0)	(PO61_0, PO81_0)	0.877133	0.969283	0.877133	1.000000	1.031690	0.026943	inf
8001	(PO79_0, PO81_0, PO76_0, PO82_0)	(PO61_0, PO60_0)	0.877133	0.969283	0.877133	1.000000	1.031690	0.026943	inf
9802	(PO78_0, PO79_0, PO61_0, PO60_0, PO81_0, PO57_0)	(PO82_0)	0.940273	0.972696	0.940273	1.000000	1.028070	0.025673	inf
9803	(PO78_0, PO79_0, PO61_0, PO60_0, PO81_0, PO82_0)	(PO57_0)	0.940273	0.977816	0.940273	1.000000	1.022688	0.020859	inf
9804	(PO78_0, PO79_0, PO61_0, PO60_0, PO57_0, PO82_0)	(PO81_0)	0.940273	0.994881	0.940273	1.000000	1.005146	0.004814	inf
9805	(PO78_0, PO79_0, PO61_0, PO81_0, PO57_0, PO82_0)	(PO60_0)	0.940273	0.994881	0.940273	1.000000	1.005146	0.004814	inf
9806	(PO78_0, PO79_0, PO60_0, PO81_0, PO57_0, PO82_0)	(PO61_0)	0.940273	0.972696	0.940273	1.000000	1.028070	0.025673	inf
4546	(PO61_0, PO76_0, PO57_0, PO60_0)	(PO82_0)	0.863481	0.972696	0.863481	1.000000	1.028070	0.023576	inf
9812	(PO78_0, PO79_0, PO61_0, PO81_0, PO57_0)	(PO82_0, PO60_0)	0.940273	0.969283	0.940273	1.000000	1.031690	0.028882	inf
9808	(PO79_0, PO61_0, PO60_0, PO81_0, PO57_0, PO82_0)	(PO78_0)	0.940273	0.977816	0.940273	1.000000	1.022688	0.020859	inf
9809	(PO78_0, PO79_0, PO61_0, PO60_0, PO81_0)	(PO57_0, PO82_0)	0.940273	0.950512	0.940273	1.000000	1.052065	0.046532	inf
9810	(PO78_0, PO79_0, PO61_0, PO60_0, PO57_0)	(PO81_0, PO82_0)	0.940273	0.969283	0.940273	1.000000	1.031690	0.028882	inf
797	(PO57_0, PO47_6, PO82_0)	(PO61_0)	0.677474	0.972696	0.677474	1.000000	1.028070	0.018498	inf
9811	(PO78_0, PO79_0, PO61_0, PO60_0, PO82_0)	(PO81_0, PO57_0)	0.940273	0.972696	0.940273	1.000000	1.028070	0.025673	inf
794	(PO61_0, PO57_0, PO47_6)	(PO82_0)	0.677474	0.972696	0.677474	1.000000	1.028070	0.018498	inf
9827	(PO79_0, PO61_0, PO81_0, PO57_0, PO82_0)	(PO78_0, PO60_0)	0.940273	0.972696	0.940273	1.000000	1.028070	0.025673	inf
9828	(PO79_0, PO60_0, PO81_0, PO57_0, PO82_0)	(PO61_0, PO78_0)	0.940273	0.950512	0.940273	1.000000	1.052065	0.046532	inf
5358	(PO79_0, PO76_0, PO57_0, PO60_0)	(PO81_0)	0.882253	0.994881	0.882253	1.000000	1.005146	0.004517	inf
3526	(PO61_0, PO78_0, PO81_0, PO57_0)	(PO82_0)	0.947099	0.972696	0.947099	1.000000	1.028070	0.025859	inf
5418	(PO61_0, PO79_0, PO81_0, PO76_0)	(PO60_0)	0.877133	0.994881	0.877133	1.000000	1.005146	0.004490	inf
1788	(PO78_0, PO81_0, PO76_0)	(PO57_0)	0.889078	0.977816	0.889078	1.000000	1.022688	0.019724	inf

	PO59	PO47	SD1	PO82	PO78	PO81	SD37	SD43	PO44	SD22	PO57	PO61	SD18	SD35	PO76	SD32	PO65	SD7	SD30	SD24	SD38	SD28	SD26	SD15	SD14	SD23	PO80	PO79	SD39	SD10	PO60	CLASS	Score
8443	4	6	12	1	0	0	0	7	2	7	0	3	1	7	0	7	1	5	0	2	5	1	3	5	2	1	1	0	4	5	0	1	0.972952
6397	5	6	8	2	0	0	0	7	0	0	0	4	2	2	0	3	0	2	0	0	2	0	0	4	4	0	1	0	5	7	0	1	0.968750
7320	3	6	1	0	1	0	0	8	2	2	3	0	0	0	0	5	1	7	0	1	2	0	2	5	4	0	1	0	6	7	0	1	0.962462
2188	4	6	7	1	0	0	0	6	2	7	0	4	0	7	0	7	1	7	0	0	6	0	3	3	6	0	1	0	3	7	0	0	0.959862
1595	3	6	38	1	0	0	2	4	0	3	0	4	4	9	0	7	0	5	7	2	2	5	2	4	3	3	1	0	5	4	0	0	0.958136
669	0	6	38	1	0	0	0	4	0	3	0	2	8	8	0	7	0	4	3	0	7	8	1	3	4	6	0	0	2	7	0	1	0.958041
5136	4	6	8	0	1	0	0	7	2	6	3	0	0	7	0	6	1	4	0	0	0	3	6	3	6	3	1	0	9	5	0	1	0.955011
2955	4	6	3	1	0	0	2	6	0	2	0	4	3	5	0	7	0	5	3	1	3	1	2	1	5	1	1	0	4	4	0	0	0.947659
5475	4	6	8	0	0	0	0	7	2	6	0	0	0	7	0	6	1	4	0	0	0	3	6	3	6	3	1	0	9	5	0	0	0.946674
7289	4	6	8	0	0	0	0	7	2	6	0	0	0	7	0	6	1	4	0	0	0	3	6	3	6	3	1	0	9	5	0	1	0.946674
5523	4	6	33	1	0	0	2	3	0	3	0	2	5	6	0	6	0	5	3	3	4	4	3	6	2	2	1	0	3	7	0	1	0.946636
2450	3	6	23	1	0	0	5	3	0	0	0	6	9	8	0	5	0	1	9	6	4	3	0	4	0	3	1	0	0	1	0	1	0.944557
7934	4	6	8	0	0	0	0	7	2	7	0	0	0	6	0	7	1	2	0	0	0	0	6	7	2	0	1	0	5	9	0	0	0.940261
8238	4	6	8	0	0	0	0	7	2	7	0	0	0	6	0	7	1	2	0	0	0	0	6	7	2	0	1	0	5	9	0	1	0.940261
7817	4	6	13	1	0	0	1	6	2	5	0	5	1	5	0	7	1	2	7	1	4	2	3	5	4	2	1	0	5	7	0	0	0.938439
5756	4	6	6	0	0	0	4	8	2	0	0	0	0	7	0	5	1	4	0	4	2	1	4	8	1	1	1	0	2	4	0	1	0.936139
3703	1	6	24	1	0	0	6	2	0	3	0	1	6	7	0	7	0	3	9	4	3	6	1	5	4	3	1	0	1	6	0	1	0.935556
4881	4	6	12	0	1	0	0	7	2	7	3	0	4	9	0	9	1	4	2	0	0	3	3	8	0	1	1	0	4	9	0	1	0.931688
8684	4	6	17	2	0	0	4	5	2	3	0	4	5	7	1	6	1	5	4	4	2	4	1	2	4	1	1	0	3	5	0	0	0.930802
626	0	6	33	1	0	0	3	3	0	2	0	4	4	5	0	7	0	4	3	1	2	3	1	4	4	3	0	0	3	7	0	1	0.930752
8960	4	6	36	2	0	0	3	3	1	2	0	2	5	4	0	4	1	5	5	2	3	3	1	1	3	2	1	0	3	4	0	0	0.929577
3917	5	6	1	0	0	0	0	8	2	2	0	0	0	0	2	5	1	7	0	1	2	0	2	5	4	0	2	0	6	7	0	1	0.926080
6393	4	6	7	0	0	0	3	6	2	4	0	0	1	3	3	7	1	2	6	1	2	2	4	4	2	1	1	0	3	6	0	1	0.925964
3509	4	5	39	1	0	0	1	5	2	3	0	4	2	7	0	7	1	7	6	3	3	4	2	2	5	3	1	0	3	9	0	1	0.921881
2797	0	6	35	1	0	0	2	5	0	2	0	4	2	5	0	6	0	7	1	2	3	2	2	5	2	1	0	0	4	7	0	1	0.920384
762	4	0	32	1	0	1	0	1	2	3	0	6	4	2	0	7	1	6	2	2	5	4	2	4	5	3	1	0	4	7	1	1	0.919718
3121	4	6	3	0	0	0	0	6	2	4	0	0	5	7	2	7	1	6	3	4	4	4	3	4	4	2	1	0	5	6	0	1	0.919297
1352	4	6	10	0	0	0	2	8	2	5	0	0	2	5	0	9	1	5	1	1	4	2	4	7	2	2	1	1	4	8	0	0	0.918962
5913	4	6	11	0	1	0	2	6	2	2	3	0	1	2	0	7	1	5	0	1	2	0	1	6	2	0	1	0	6	7	0	1	0.917849
4526	4	6	10	0	0	0	3	8	2	7	0	0	1	6	0	9	1	3	1	1	2	2	6	8	1	1	1	0	5	7	0	0	0.917394
7957	3	6	6	0	0	0	0	8	2	2	0	0	0	2	0	7	1	7	0	0	0	0	2	6	3	0	1	0	7	9	0	1	0.917279
7986	4	6	8	0	0	0	1	7	2	5	0	0	2	5	1	6	1	7	0	1	4	2	2	4	4	1	1	0	4	6	0	1	0.916449
8822	6	6	11	0	0	1	2	7	2	1	0	0	2	7	0	3	1	5	1	2	4	2	1	3	6	0	2	0	1	6	2	1	0.916177
6540	4	0	8	0	0	0	0	7	2	6	0	0	0	7	0	6	1	4	0	0	0	3	6	3	6	3	1	0	9	5	0	1	0.915715
5170	4	6	12	0	0	0	0	7	2	7	0	0	4	9	0	9	1	4	2	0	0	3	3	8	0	1	1	0	4	9	0	1	0.915604
3741	4	6	12	0	0	0	0	7	2	7	0	0	4	9	0	9	1	4	2	0	0	3	3	8	0	1	1	0	4	9	0	1	0.915604
6618	4	6	33	0	0	0	1	3	2	1	0	0	8	9	2	8	1	5	3	1	3	8	1	5	4	8	1	0	5	8	0	1	0.915492
5579	5	6	1	0	0	0	0	8	2	2	0	0	0	2	0	6	1	7	0	0	0	0	0	2	7	0	1	0	5	7	0	1	0.915282
2174	5	6	1	0	0	0	0	8	2	2	0	0	0	2	0	6	1	7	0	0	0	0	0	2	7	0	1	0	5	7	0	0	0.915282
1319	4	6	12	0	0	0	2	7	2	7	0	0	6	9	0	9	1	2	0	2	0	6	2	9	0	0	1	0	5	9	0	1	0.914638

	PO59	PO47	SD1	PO82	PO78	SD37	SD43	PO44	SD22	PO57	PO61	SD18	SD35	PO76	SD32	PO65	SD7	SD30	SD24	SD38	SD28	SD26	SD15	SD14	SD23	PO80	PO79	SD39	SD10	Score
2188	4	6	7	1	0	0	6	2	7	0	4	0	7	0	7	1	7	0	0	6	0	3	3	6	0	1	0	3	7	0.959862
1595	3	6	38	1	0	2	4	0	3	0	4	4	9	0	7	0	5	7	2	2	5	2	4	3	3	1	0	5	4	0.958136
2955	4	6	3	1	0	2	6	0	2	0	4	3	5	0	7	0	5	3	1	3	1	2	1	5	1	1	0	4	4	0.947659
5475	4	6	8	0	0	0	7	2	6	0	0	0	7	0	6	1	4	0	0	0	3	6	3	6	3	1	0	9	5	0.946674
7934	4	6	8	0	0	0	7	2	7	0	0	0	6	0	7	1	2	0	0	0	0	6	7	2	0	1	0	5	9	0.940261
7817	4	6	13	1	0	1	6	2	5	0	5	1	5	0	7	1	2	7	1	4	2	3	5	4	2	1	0	5	7	0.938439
8684	4	6	17	2	0	4	5	2	3	0	4	5	7	1	6	1	5	4	4	2	4	1	2	4	1	1	0	3	5	0.930802
8960	4	6	36	2	0	3	3	1	2	0	2	5	4	0	4	1	5	5	2	3	3	1	1	3	2	1	0	3	4	0.929577
1352	4	6	10	0	0	2	8	2	5	0	0	2	5	0	9	1	5	1	1	4	2	4	7	2	2	1	1	4	8	0.918962
4526	4	6	10	0	0	3	8	2	7	0	0	1	6	0	9	1	3	1	1	2	2	6	8	1	1	1	0	5	7	0.917394
2174	5	6	1	0	0	0	8	2	2	0	0	0	2	0	6	1	7	0	0	0	0	0	2	7	0	1	0	5	7	0.915282
1665	3	6	8	0	0	0	7	0	6	0	0	0	7	0	6	0	4	0	0	0	3	6	3	6	3	1	0	9	5	0.913704
8489	4	6	8	0	0	0	7	0	0	0	0	2	2	0	3	0	2	0	0	2	0	0	4	4	0	1	0	5	7	0.912148
2786	4	6	8	0	0	0	7	2	9	0	0	5	2	0	6	1	5	0	0	9	5	4	9	0	0	1	0	0	9	0.908097
8620	4	6	8	0	0	0	7	2	9	0	0	5	2	0	6	1	5	0	0	9	5	4	9	0	0	1	0	0	9	0.908097
2099	4	6	8	0	1	0	7	2	0	3	0	2	2	1	3	1	2	0	0	2	0	0	4	4	0	1	0	5	7	0.906925
5607	5	6	1	0	0	0	8	2	0	0	0	2	2	0	3	1	2	0	0	2	0	0	4	4	0	1	0	5	7	0.905649
4619	4	6	8	0	0	1	7	2	5	0	0	2	5	0	6	1	7	0	1	4	2	2	4	4	1	1	0	4	6	0.905498
4742	4	6	6	0	0	0	8	2	2	0	0	2	0	0	5	1	4	0	0	3	2	4	4	5	0	1	0	6	9	0.905323
4592	4	6	8	0	0	0	7	2	0	0	0	2	2	0	3	1	2	0	0	2	0	0	4	4	0	1	0	5	7	0.904050
1984	4	6	8	0	0	0	7	2	0	0	0	2	2	0	3	1	2	0	0	2	0	0	4	4	0	1	0	5	7	0.904050
482	4	6	8	0	0	0	7	2	6	0	0	4	9	0	9	1	3	2	0	0	4	2	9	0	2	1	0	4	9	0.902116
1499	4	6	8	0	0	0	7	0	9	0	0	5	2	0	6	0	5	0	0	9	5	4	9	0	0	1	0	0	9	0.901405
6766	4	6	1	0	0	0	8	2	2	0	0	0	0	0	5	1	7	0	1	2	0	2	5	4	0	1	0	6	7	0.897572
8570	4	6	1	0	0	0	8	2	2	0	0	0	0	0	5	1	7	0	1	2	0	2	5	4	0	1	0	6	7	0.897572
715	4	6	1	0	0	0	8	1	0	0	0	2	2	0	3	1	2	0	0	2	0	0	4	4	0	1	0	5	7	0.896361
4712	4	6	8	0	0	0	7	2	7	0	0	0	7	0	7	1	7	0	0	6	0	3	3	6	0	1	0	3	7	0.895466
6528	4	6	8	0	0	0	7	2	7	0	0	0	7	0	7	1	7	0	0	6	0	3	3	6	0	1	0	3	7	0.895466
2289	5	6	1	0	0	1	8	2	0	0	0	1	0	0	6	1	6	1	0	0	0	1	1	8	1	1	0	5	9	0.893538
7511	3	6	6	0	0	0	8	0	2	0	0	0	2	0	7	0	7	0	0	0	0	2	6	3	0	1	0	7	9	0.892740
865	3	6	2	0	0	2	6	2	0	0	0	0	3	0	7	1	5	6	5	4	4	0	0	9	0	1	0	4	9	0.892472
5092	4	6	6	0	0	0	8	0	1	0	0	1	5	0	7	0	5	0	3	1	1	2	6	3	1	1	0	3	6	0.888789
8905	4	6	1	0	0	1	8	2	1	0	0	1	5	0	5	1	5	1	1	1	1	3	1	8	1	1	0	3	5	0.886274
4061	3	6	8	0	0	0	7	0	9	0	0	5	2	0	6	0	5	0	0	9	5	4	9	0	0	1	0	0	9	0.885652
6869	4	6	41	0	0	0	4	2	0	0	0	0	5	0	6	1	4	4	0	0	0	0	4	4	0	1	0	9	7	0.885312
1078	4	6	35	0	0	2	5	2	2	0	0	2	5	2	6	1	7	1	2	3	2	2	5	2	1	1	0	4	7	0.885086
6596	4	6	12	0	0	2	7	2	6	0	0	2	4	0	9	1	3	0	2	3	1	2	5	2	0	1	0	5	5	0.882813
4036	4	6	8	0	0	0	7	2	4	0	0	4	5	0	8	1	7	0	1	6	4	2	7	2	2	1	0	3	9	0.881914
9169	4	6	8	0	0	0	7	2	4	0	0	4	5	0	8	1	7	0	1	6	4	2	7	2	2	1	0	3	9	0.881914
4807	4	6	8	0	0	2	7	2	2	0	0	1	3	0	7	1	4	6	1	4	2	0	2	6	0	1	0	4	7	0.880163

	SD1	SD2	SD3	SD4	SD5	SD6	SD7	SD8	SD9	SD10	...	PO79	PO80	PO83	CLASS	Cluster
41	11	1	3	3	3	2	7	0	0	9	...	0	2	1	1	1
45	38	1	3	3	9	0	5	1	3	7	...	0	1	0	1	0
57	12	1	3	2	3	0	6	0	3	7	...	1	1	0	1	1
97	9	1	2	3	3	0	6	1	2	7	...	0	0	0	1	1
98	36	1	2	4	8	2	4	2	2	7	...	0	0	0	1	0

	PO59	PO47	SD1	SD37	SD43	PO44	SD22	...	SD26	SD15	SD14	SD23	PO80	SD39	SD10
0	5	6	33	0	3	0	2	...	1	6	2	5	1	5	7
1	2	0	37	2	4	2	5	...	2	5	4	0	1	5	6
2	2	6	37	4	4	2	7	...	5	2	4	0	1	0	3
3	2	6	9	1	4	0	3	...	2	4	3	1	1	3	5
4	6	0	40	0	3	0	0	...	0	4	4	0	1	9	7

	PO59	PO47	SD1	SD37	SD43	PO44	SD22	...	SD28	SD26	SD15	SD14	SD23	PO80	PO79	SD39	SD10
41	4	6	11	2	6	2	4	...	2	2	4	3	0	2	0	3	9
45	3	6	38	4	4	2	2	...	5	1	5	2	3	1	0	1	7
57	4	0	12	2	7	2	5	...	4	3	6	2	2	1	1	4	7
97	0	0	9	0	4	0	5	...	0	8	4	1	1	0	0	4	7
98	0	0	36	2	3	0	3	...	4	1	3	4	1	0	0	3	7

	PO59_0	PO59_3	PO59_4	PO47_0	PO47_6	...	SD10_7	SD10_9	PO60_0
41	0	0	1	0	1	...	0	1	1
45	0	1	0	0	1	...	1	0	1
57	0	0	1	1	0	...	1	0	1
97	1	0	0	1	0	...	1	0	1
98	1	0	0	1	0	...	1	0	1

	PO59	PO47	SD1	SD37	SD43	PO44	SD22	SD18	SD35	SD32	PO65	SD7	SD30	SD24	SD38	SD28	SD26	SD15	SD14	SD23	PO80	SD39	SD10
0	5	6	33	0	3	0	2	7	8	8	0	5	1	2	4	6	1	6	2	5	1	5	7
1	2	0	37	2	4	2	5	4	6	7	2	4	2	4	0	5	2	5	4	0	1	5	6
2	2	6	37	4	4	2	7	4	9	7	1	4	7	2	5	4	5	2	4	0	1	0	3
3	2	6	9	1	4	0	3	2	7	9	0	3	5	2	5	4	2	4	3	1	1	3	5
4	6	0	40	0	3	0	0	0	5	6	0	4	4	0	0	0	0	4	4	0	1	9	7

Lab 2 : AssurExperts INC ¶

Lab 2 : AssurExperts INC